The present invention relates to the field of non-destructive evaluation of defects in a solid sample, and more particularly to a method and system for analyzing thermographic image data to detect sub-surface defects.
Non-destructive evaluation of samples for sub-surface defects can be used to check samples for weld integrity, delamination, and other structural defects that are not visible from observing the surface of the sample. Active thermography has been viewed as one option for non-destructive testing. Generally, active thermography involves heating or cooling the sample to create a difference between the sample temperature and the ambient temperature and then observing the surface temperature of the sample as it returns to ambient temperature. For purposes of illustration only, the description will focus on the heating example, where the sample is heated via any means (e.g., flashlamps, heat lamps, forced hot air, hot air guns, electrical current, ultrasonic excitation, etc.) and then the allowed to cool. In this type of process, the subsequent cooling is monitored using an infrared camera to detect any anomalies in the cooling behavior, which would be caused by sub-surface defects (e.g., voids, delaminations, inclusions, etc.) blocking the diffusion of heat from the sample surface to the sample""s interior. More particularly, these defects cause the surface immediately above the defect to cool at a different rate that the surrounding defect-free areas.
As the sample cools, the infrared camera monitors and records an image sequence indicating the surface temperature, thereby creating a record of the changes in the surface temperature over time. The sub-surface defects can therefore be observed by viewing the output of the infrared camera through a display device or by capturing individual frames at selected times after the sample has been heated. This method of visual defect identification tends to be subjective, however, and is not suitable for automation of the defect detection process. Further, it is not possible to measure the depth of the defects simply by viewing the infrared images.
There have been attempts to determine the depth of a defect via processing and analysis of the data from the infrared camera and also to automate the defect detection process. In some cases, the data from the infrared camera is transferred to a computer for processing and analysis to detect variations in the cooling behavior or to perform mathematical operations on the data to determine the depth of the sub-surface defect or other defect properties. These types of calculations, however, often require expensive low noise, high-speed digital infrared cameras. Further, the cumbersome nature of having a computer attached to the camera for conducting calculations makes the combination impractical for applications outside of a laboratory, such as field inspections. Also, infrared data sequences of thermal decay typically used in non-destructive testing tend to be difficult to manipulate mathematically due to low signal-to-noise ratios and large dynamic range and also requires a great deal of storage space.
One attempt at automating the defect detection process involves analyzing the contrast between each pixel in the image and a reference pixel or pixel group to generate a curve representing the amount of contrast between each pixel and the reference. This method requires identification of a reference point on the sample. The reference point can be a defect-free area of the sample, a separate defect-free sample that is placed in the camera""s field of view, or the mean of the entire field of view of the camera. The temperature-time history of this reference pixel or pixel group is subtracted from each pixel in the image to generate a contrast vs. time plot.
Any large differences between any given pixel and the reference indicates the presence of a defect and will exhibit itself as a peak in the plot. The contrast vs. time plot can be measured with respect to the time at which the peak occurs, the time at which a maximum ascending slope occurs, and/or a moment of the curve for each pixel. Other options, such as generating and displaying the contrast vs. time plot with a reference plot and checking the point at which the two plots separate, is also an option.
Contrast-based methods tend to be flawed, however. In addition to the data storage problems noted above due to the large size of the infrared image data files, contrast-based methods require the identification of a defect-free region on the sample as a reference point. This requirement is often not realistic for some samples if, for example, the size of the defect is larger than the infrared camera""s field of view. In such a case, there is no defect-free area available that can act as a reference for a given region. Further, if the entire sample exhibits a defect (e.g., a large delamination running underneath the entire surface of the sample), there is no contrast between any region of the sample because the whole sample is equally or nearly equally defective.
Contrast-based methods that rely on the mean of the entire field of view as a reference have also been used, but this method assumes that the defect area in the field is small enough so that it will not appreciably affect the mean. If a defect or group of defects occupy a large portion of the field of view, the contrast method will be ineffective because the mean value will be influenced to a large degree by the defects, reducing any appreciable difference between the defect area and the mean when the contrast value is calculated.
Regardless of the specific reference value used in detecting defects, the results obtained using contrast-based methods depends strongly on the choice of reference region on the sample. More particularly, the results obtained in contrast-based methods can be adjusted by simply changing the location of the reference region.
Further, in evaluating the results from both the contrast-based methods and the data obtained directly from the infrared camera, identifying the time at which a peak slope occurs (indicating the presence of a defect) may be difficult because the signals are often inherently noisy, requiring the defect detection system to discriminate pixels associated with defects from noisy pixels.
There is a need for a non-destructive defect detection system and method that reduces the size and complexity of the temperature-time history of image data without sacrificing its usefulness in detecting the location and physical characteristics of sub-surface defects.
There is also a need for a non-destructive defect detection system that does not require obtaining a reference value to detect defects by locating areas in which there is a contrast between the reference and the sample being evaluated.
There is further a need to improve the signal-to-noise ratio of the camera output without sacrificing spatial resolution.
Accordingly, the present invention is directed to a system and method for non-destructive detection of subsurface defects that avoids the problems encountered by currently known systems. The inventive system and method determines the response of a monotonically changing characteristic, such as temperature, of a sample over time and captures a plurality of images as the sample characteristic changes. The images are used to generate a data array for each pixel in the images. The data array corresponds to a logarithm of the pixel amplitude and a logarithm of a given time so that a plot of monotonically changing characteristic will be roughly linear.
A polynomial is then fitted to the data array. The polynomial contains at least two polynomial coefficients, and typically from five to seven polynomial coefficients. These coefficients are analyzed instead of the raw data in the data array, simplifying mathematical manipulation of the data. Because the system preferably stores and evaluates the coefficients representing the sample characteristic, and not the sample characteristic data itself, the amount of data that needs to be stored is greatly reduced. In one embodiment, defects can be detected by checking whether the second derivative of the polynomial has a zero crossing. Further, the polynomial data has a higher signal-to-noise ratio than the original data, allowing the polynomial data to serve as a less noisy substitute for the raw image data when the polynomial data is converted from the logarithmic domain back into the linear domain.